Chapter 6 describes multiple approaches to group observations with similar characteristics, including:

• Issues to consider when grouping data
• Hierarchical agglomerative clustering
• k-means clustering
• Associative rules
• Decisions trees

• General data mining grouping approaches and outlier detection:
  • Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations Second Edition (2005) by I.H. Witten and E. Frank
  • Data Mining: Concepts and Techniques, Second Edition (2005) by J. Han and M. Kamber
  • Principles of Data Mining (2001) by D.J. Hand , H. Mannila and P. Smyth
     
• Similarity methods and clustering approaches:
• Cluster Analysis Fourth Edition (2001) by B.S. Everitt, S. Landau and M. Leese
  
  
• Decision trees :
  • C4.5: Programs for Machine Learning (1993) by J.R. Quinlan
    
    
• Additional grouping approaches:
  • The Elements of Statistical Learning (2003) by T.R. Hastie, J.H. Tibshirani and J.H Friedman

• Clustering
• Associative rules
• Decision trees